Abstract
ML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(Δ), correlation length along x-direction (A X ), and correlation length along y-direction (A Y ), the metrics for device performance such as on-state drive current, off-state leakage current, threshold voltage, and subthreshold swing can be computing-efficiently estimated with the ANN model.
Highlights
For the last a few decades, complementary metal oxide semiconductor (CMOS) technology has been successfully evolved with the adoption of new techniques such as stress engineering in 90 nm technology node and beyond [1], high-k/metal-gate in 45 nm technology node and beyond [2], and 3-D advanced device structure in 22 nm technology node and beyond [3]
Process-induced random variations, have negatively affected the manufacturability of CMOS devices, and thereby, it would significantly hinder the evolution of CMOS technology [4]
As the device architecture becomes more complicated (in reality, multiple bridge channel field effect transistor (MBCFET), stacked nano-wire FET, stacked nano-slab FET, etc. for 3 nm CMOS technology node [8] and beyond), understanding the impact of line edge roughness (LER) on device performance is desperately required in developing variation-robust silicon device at 3 nm technology node and beyond [9]
Summary
For the last a few decades, complementary metal oxide semiconductor (CMOS) technology has been successfully evolved with the adoption of new techniques such as stress engineering in 90 nm technology node and beyond [1], high-k/metal-gate in 45 nm technology node and beyond [2], and 3-D advanced device structure in 22 nm technology node and beyond [3]. The root-causes of process-induced random variation are classified as (i) line edge roughness (LER), (ii) random dopant fluctuation (RDF), and (iii) work function variation (WFV) [5]. Shin: ML-Based Model to Characterize the LER-Induced Random Variation in FinFET. TCAD (Technology Computer Aided Design)based method has been adopted to propose model for finely and accurately predicting the impact of LER [13]. Due to many technical barriers in developing a new compact model, the compact model for analyzing the impact of LER [14], [15] would not be timely developed, even though the LER on the fin sidewall of FinFET should be modeled for two-dimensionally characterizing/understanding the sidewall surface [7], [13]. Using Machine Learning (ML) technique, simple but eye-catching novel approach with reasonable accuracy is proposed in this work, to provide an alternative device solution for predicting the process-induced variation.
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